Physics-informed neural networks (PINNs) [1]} are a exciting new tool for blending data and known physical laws in the form of differential equations. They have been successfully applied to multiple physical domains such as fluid mechanics [2]}, solid mechanics [3]}, heat transfer [4]} and biomedical engineering [5]}, [6]}, [7]}, to name a few. Even though this technique can be used to solve forward problems, they tend to excel when performing inverse problems. Since their inception, there have been multiple attempts to improve PINNs, in areas such as training strategies [8]}, [9]} and activation functions [10]}.
